Multi-Agent-AI-Models-and-Path-Planning provides a comprehensive toolkit for developing and testing multi-agent systems combined with classical and modern path planning methods. It includes implementations of algorithms such as A*, Dijkstra, RRT, and potential fields, alongside customizable agent behavior models. The framework features simulation and visualization modules, allowing seamless scenario creation, real-time monitoring, and performance analysis. Designed for extensibility, users can plug in new planning algorithms or agent decision models to evaluate cooperative navigation and task allocation in complex environments.

JADE is a Java-based agent framework enabling developers to create, deploy, and manage multiple autonomous software agents across distributed environments. Each agent runs within a container, communicates via FIPA-compliant Agent Communication Language (ACL), and can register services with a Directory Facilitator for discovery. Agents execute predefined behaviors or dynamic tasks and can migrate between containers using Remote Method Invocation (RMI). JADE supports ontology definitions for structured message content and provides graphical tools for monitoring agent states and message exchanges. Its modular architecture allows integration with external services, databases, and REST interfaces, making it suitable for developing simulations, IoT orchestrations, negotiation systems, and more. The framework’s extensibility and compliance with industry standards streamline the implementation of complex multi-agent systems.

Agent-Oriented Architecture (AOA) is a robust framework that equips developers with tools to build and maintain intelligent multi-agent systems. Agents encapsulate state, behaviors, and interaction patterns, communicating via an asynchronous message bus. AOA includes modules for agent registration, discovery, and matchmaking, enabling dynamic service composition. Behavior modeling supports finite-state machines, goal-driven planning, and event-driven triggers. The framework handles agent lifecycle events like creation, suspension, migration, and termination. Built-in monitoring and logging facilitate performance tuning and debugging. AOA’s pluggable transport layer supports TCP, HTTP, and custom protocols, making it adaptable for on-premise, cloud, or edge deployments. Integration with popular libraries ensures seamless data processing and AI model integration.